Genetic Main Effects Research Articles

A model-free approach for detecting interactions in genetic association studies

Over the past few decades, genome-wide association studies analyzed by efficient statistical procedures have successfully identified single-nucleotide polymorphisms (SNPs) that are associated with complex traits or human diseases. However, due to the overwhelming number of SNPs, most approaches have focused on additive genetic model without genome-wide SNP-SNP interactions. In this study, we propose an efficient statistical procedure in a genetic model-free framework for detecting SNPs exhibiting main genetic effects as well as epistatic interactions. Specifically, the association between phenotype and genotype is characterized by an unknown function to be estimated using nonparametric techniques, and a two-stage non-parametric independence screening procedure is proposed to sequentially identify potentially important main genetic effects and interactions. Finally, the subset of genetic predictors implied by two-stage non-parametric independence screening is analyzed by penalized regressions such as LASSO, and a final model is identified. In this framework, specific genetic model is not assumed and interactions are not only among marginally important SNPs. Therefore, SNPs that are involved in genetic regulatory networks but missed by previous studies are expected to be recognized. In simulation studies, we show that the procedure is computationally efficient and has an outstanding finite sample performance in selecting potential SNPs as well as SNP-SNP interactions. A real data analysis further indicates the importance of epistatic interactions in explaining body mass index.

Interaction between the NOS3 gene and obesity as a determinant of risk of type 2 diabetes: the Atherosclerosis Risk in Communities study.

Endothelial nitric oxide synthase 3 (NOS3) catalyzes the production of nitric oxide from L-arginine in endothelial cells. Obesity is a modifiable risk factor for diabetes, and obese individuals have been reported to have reduced nitric oxide availability compared to controls whose weight is in the normal range. Since homozygous carriers of the NOS3 G894T variant are predicted to have decreased enzyme activity, the association between NOS3 genotype and type 2 diabetes, and possible effect modification by body mass index (BMI) were evaluated. The prevalence of diabetes and BMI was determined at baseline in 14,374 participants 45–66 years of age from the prospective biracial population-based Atherosclerosis Risk in Communities (ARIC) Study of the development of atherosclerosis in four communities in the United States. Individuals with a BMI ≥30 kg/m2 were considered obese. Those subjects not meeting the case definition were the comparison groups for the 728 African American and 980 white participants with diabetes. Multivariable logistic regression models adjusted for age, sex, and field center were used to test for main genetic effects and interaction with obesity. Although the NOS3 G894T variant was not independently associated with diabetes in either African Americans or whites, significant interaction between BMI and the NOS3 polymorphism indicated that obesity was an effect modifier of diabetes risk for white individuals with the TT genotype (odds ratio (OR) for interaction = 1.65, p = 0.04). In stratified analyses, homozygosity for the NOS3 T allele in obese white participants but not in those whose BMI <30 kg/m2 was associated with an elevated risk of diabetes (OR = 1.47, p = 0.02) when compared to the common GG genotype. These results suggest that interaction between obesity and NOS3 genotype may be a determinant of diabetes case status in whites in the ARIC cohort. Replication in other populations will be required to confirm these observations.

Natural and orthogonal model for estimating gene–gene interactions applied to cutaneous melanoma

Epistasis, or gene-gene interaction, results from joint effects of genes on a trait; thus, the same alleles of one gene may display different genetic effects in different genetic backgrounds. In this study, we generalized the coding technique of a natural and orthogonal interaction (NOIA) model for association studies along with gene-gene interactions for dichotomous traits and human complex diseases. The NOIA model which has non-correlated estimators for genetic effects is important for estimating influence from multiple loci. We conducted simulations and data analyses to evaluate the performance of the NOIA model. Both simulation and real data analyses revealed that the NOIA statistical model had higher power for detecting main genetic effects and usually had higher power for some interaction effects than the usual model. Although associated genes have been identified for predisposing people to melanoma risk: HERC2 at 15q13.1, MC1R at 16q24.3 and CDKN2A at 9p21.3, no gene-gene interaction study has been fully explored for melanoma. By applying the NOIA statistical model to a genome-wide melanoma dataset, we confirmed the previously identified significantly associated genes and found potential regions at chromosomes 5 and 4 that may interact with the HERC2 and MC1R genes, respectively. Our study not only generalized the orthogonal NOIA model but also provided useful insights for understanding the influence of interactions on melanoma risk.

Genetic analysis of plant height using two immortalized populations of “CRI12 × J8891” in Gossypium hirsutum L.

Plant height is an important plant architecture trait that determines the canopy structure, photosynthetic capacity and lodging resistance of upland cotton populations. To understand the genetic basis of plant height for marker-assisted breeding, quantitative trait loci (QTL) analysis was conducted based on the genetic map of recombinant inbred lines (RILs) derived from the cross “CRI12 × J8891” (Gossypium hirsutum L.). Three methods, including composite interval mapping, multiple interval mapping and multi-marker joint analysis, were used to detect QTL across multiple environments in the RILs and in the immortalized F2 population developed through intermating between RILs. A total of 19 QTL with genetic main effects and/or genetic × environment interaction effects were identified on 15 chromosomes or linkage groups, each explaining 5.8–14.3 % of the phenotypic variation. Five digenic epistatic QTL pairs, mainly involving additive × additive and/or dominance × dominance, were detected in different environments. Seven out of eight interacting loci were main-effect QTL, suggesting that these loci act as major genes as well as modifying genes in the expression of plant height. The results demonstrate that additive effects, dominance and epistasis are all important for the genetic constitution of plant height, with additive effects playing a more important role in reducing plant height. QTL showing stability across environments that were repeatedly detected by different methods can be used in marker-assisted breeding.

Interaction between genetic variants and exposure to Hurricane Katrina on post-traumatic stress and post-traumatic growth: A prospective analysis of low income adults

BackgroundThere is considerable variation in psychological reactions to natural disasters, with responses ranging from relatively mild and transitory symptoms to severe and persistent posttraumatic stress (PTS). Some survivors also report post-traumatic growth (PTG), or positive psychological changes due to the experience and processing of the disaster and its aftermath. Gene–environment interaction (GxE) studies could offer new insight into the factors underlying variability in post-disaster psychological responses. However, few studies have explored GxE in a disaster context. MethodsWe examined whether ten common variants in seven genes (BDNF, CACNA1C, CRHR1, FKBP5, OXTR, RGS2, SLC6A4) modified associations between Hurricane Katrina exposure and PTS and PTG. Data were from a prospective study of 205 low-income non-Hispanic Black parents residing in New Orleans prior to and following Hurricane Katrina. ResultsWe found a significant association (after correction) between RGS2 (rs4606; p=0.0044) and PTG, which was mainly driven by a cross-over GxE (p=0.006), rather than a main genetic effect (p=0.071). The G (minor allele) was associated with lower PTG scores for low levels of Hurricane exposure and higher PTG scores for moderate and high levels of exposure. We also found a nominally significant association between variation in FKBP5 (rs1306780, p=0.0113) and PTG, though this result did not survive correction for multiple testing. LimitationsAlthough the inclusion of low-income non-Hispanic Black parents allowed us to examine GxE among a highly vulnerable group, our findings may not generalize to other populations or groups experiencing other natural disasters. Moreover, not all participants invited to participate in the genetic study provided saliva. ConclusionsTo our knowledge, this is the first study to identify GxE in the context of post-traumatic growth. Future studies are needed to clarify the role of GxE in PTS and PTG and post-disaster psychological responses, especially among vulnerable populations.

Testing gene‐environment interactions in family‐based association studies using trait‐based ascertained samples

The study of gene-environment interactions is an increasingly important aspect of genetic epidemiological investigation. Historically, it has been difficult to study gene-environment interactions using a family-based design for quantitative traits or when parent-offspring trios were incomplete. The QBAT-I provides researchers a tool to estimate and test for a gene-environment interaction in families of arbitrary structure that are sampled without regard to the phenotype of interest, but is vulnerable to inflated type I error if families are ascertained on the basis of the phenotype. In this study, we verified the potential for type I error of the QBAT-I when applied to samples ascertained on a trait of interest. The magnitude of the inflation increases as the main genetic effect increases and as the ascertainment becomes more extreme. We propose an ascertainment-corrected score test that allows the use of the QBAT-I to test for gene-environment interactions in ascertained samples. Our results indicate that the score test and an ad hoc method we propose can often restore the nominal type I error rate, and in cases where complete restoration is not possible, dramatically reduce the inflation of the type I error rate in ascertained samples.

Environmental Confounding in Gene-Environment Interaction Studies

We show that, in the presence of uncontrolled environmental confounding, joint tests for the presence of a main genetic effect and gene-environment interaction will be biased if the genetic and environmental factors are correlated, even if there is no effect of either the genetic factor or the environmental factor on the disease. When environmental confounding is ignored, such tests will in fact reject the joint null of no genetic effect with a probability that tends to 1 as the sample size increases. This problem with the joint test vanishes under gene-environment independence, but it still persists if estimating the gene-environment interaction parameter itself is of interest. Uncontrolled environmental confounding will bias estimates of gene-environment interaction parameters even under gene-environment independence, but it will not do so if the unmeasured confounding variable itself does not interact with the genetic factor. Under gene-environment independence, if the interaction parameter without controlling for the environmental confounder is nonzero, then there is gene-environment interaction either between the genetic factor and the environmental factor of interest or between the genetic factor and the unmeasured environmental confounder. We evaluate several recently proposed joint tests in a simulation study and discuss the implications of these results for the conduct of gene-environment interaction studies.

QTL Mapping Based on Different Genetic Systems for Essential Amino Acid Contents in Cottonseeds in Different Environments

Cottonseeds are rich in various essential amino acids. However, the inheritance of them at molecular level are still not defined across various genetic systems. In the present study, using a newly developed mapping model that can analyze the embryo and maternal main effects as well as QTL × environment interaction effects on quantitative quality trait loci (QTLs) in cottonseeds, a study on QTL located in the tetraploid embryo and tetraploid maternal plant genomes for essential amino acid contents in cottonseeds under different environments was carried out, using the immortal F2 (IF2) populations from a set of 188 recombinant inbred lines derived from an intraspecific hybrid cross of two upland cotton germplasms HS46 and MARKCBUCAG8US-1-88 as experimental materials. The results showed a total of 35 QTLs associated with these quality traits in cottonseeds. Nineteen QTLs were subsequently mapped on chromosome 5, 6 and 8 in sub-A genome and chromosome 15, 18, 22 and 23 in sub-D genome. Eighteen QTLs were also found having QTL × environment (QE) interaction effects. The genetic main effects from QTLs located on chromosomes in the embryo and maternal plant genomes and their QE effects in different environments were all important for these essential amino acids in cottonseeds. The results suggested that the influence of environmental factors on the expression of some QTLs located in different genetic systems should be considered when improving for these amino acids. This study can serve as the foundation for the improvement of these essential amino acids in cottonseeds.

Gene—Environment Interactions in Major Depressive Disorder

Family, twin, and epidemiologic studies have suggested that both genes and environment are important risk factors for the development of major depressive disorder (MDD). In the absence of consistent and strong main genetic effects, numerous studies have supported gene-environment interactions in this disorder. While the impact of negative environmental factors, such as early life stress, traumatic experiences, and negative life events have been established as risk factors, they are not sufficient to predict MDD. This article will review evidence suggesting that genetic variants moderate the effects of adversities on the development of MDD, with a focus on the importance of careful characterization of the stressful life events as well as systemic and molecular mechanisms that potentially mediate these gene-environment interactions.

Differential susceptibility to prevention: GABAergic, dopaminergic, and multilocus effects

Randomized prevention trials provide a unique opportunity to test hypotheses about the interaction of genetic predispositions with contextual processes to create variations in phenotypes over time. Using two longitudinal, randomized prevention trials, molecular genetic and alcohol use outcome data were gathered from more than 900 youths to determine whether prevention program participation would, across 2 years, moderate genetic risk for increased alcohol use conferred by the dopaminergic and GABAergic systems. We found that (a) variance in dopaminergic (DRD2, DRD4, ANKK1) and GABAergic (GABRG1, GABRA2) genes forecast increases in alcohol use across 2 years, and (b) youths at genetic risk who were assigned to the control condition displayed greater increases in alcohol use across 2 years than did youths at genetic risk who were assigned to the prevention condition or youths without genetic risk who were assigned to either condition. This study is unique in combining data from two large prevention trials to test hypotheses regarding genetic main effects and gene × prevention interactions. Focusing on gene systems purported to confer risk for alcohol use and abuse, the study demonstrated that participation in efficacious prevention programs can moderate genetic risk. The results also support the differential susceptibility hypothesis that some youths, for genetic reasons, are more susceptible than others to both positive and negative contextual influences.

Exploring genome-wide – dietary heme iron intake interactions and the risk of type 2 diabetes

Aims/hypothesis: Genome-wide association studies have identified over 50 new genetic loci for type 2 diabetes (T2D). Several studies conclude that higher dietary heme iron intake increases the risk of T2D. Therefore we assessed whether the relation between genetic loci and T2D is modified by dietary heme iron intake.Methods: We used Affymetrix Genome-Wide Human 6.0 array data [681,770 single nucleotide polymorphisms (SNPs)] and dietary information collected in the Health Professionals Follow-up Study (n = 725 cases; n = 1,273 controls) and the Nurses’ Health Study (n = 1,081 cases; n = 1,692 controls). We assessed whether genome-wide SNPs or iron metabolism SNPs interacted with dietary heme iron intake in relation to T2D, testing for associations in each cohort separately and then meta-analyzing to pool the results. Finally, we created 1,000 synthetic pathways matched to an iron metabolism pathway on number of genes, and number of SNPs in each gene. We compared the iron metabolic pathway SNPs with these synthetic SNP assemblies in their relation to T2D to assess if the pathway as a whole interacts with dietary heme iron intake.Results: Using a genomic approach, we found no significant gene–environment interactions with dietary heme iron intake in relation to T2D at a Bonferroni corrected genome-wide significance level of 7.33 ×10-8 (top SNP in pooled analysis: intergenic rs10980508; p = 1.03 × 10-6). Furthermore, no SNP in the iron metabolic pathway significantly interacted with dietary heme iron intake at a Bonferroni corrected significance level of 2.10 × 10-4 (top SNP in pooled analysis: rs1805313; p = 1.14 × 10-3). Finally, neither the main genetic effects (pooled empirical p by SNP = 0.41), nor gene – dietary heme–iron interactions (pooled empirical p-value for the interactions = 0.72) were significant for the iron metabolic pathway as a whole.Conclusions: We found no significant interactions between dietary heme iron intake and common SNPs in relation to T2D.

English

&nbsp; The present study was carried out to determine non-genetics effects on the growth performance (birth, weaning, one year weight and weight gains) of Horro cattle and their crosses (Friesian-Horro (FH) and Jersey-Horro (JH)) at Bako Agricultural Research center. The data used in the study included weight records of animals born between 1980 and 2008. Least squares means were analyzed using General Linear Model (GLM) of Statistical Analyses System (SAS). Apart from Breed effects, sex, year of birth and parity were the main non genetic effects that influenced birth weight (BW), weaning weight (WW), one year weight (YW), pre-weaning average daily gain (DG) and post-weaning average daily gain (PDG). The overall mean of birth, weaning and one year weight and pre-weaning and post-weaning average daily gains of Horro and their crosses were 19.6&plusmn;0.16 kg, 43.8 &plusmn;8.5 kg and 84.8&plusmn;21 kg, 407.4&plusmn;20.6 g and 263.7&plusmn;54.3 g, respectively. The least squares means birth weight, weaning weight and one year were: 18.34&plusmn;0.14 kg, 39.8&plusmn;0.39 kg and 70.5&plusmn;1.14 kg for Horro, 22.13&plusmn;0.14 kg, 47.5&plusmn;0.38 kg and 94.7&plusmn;1.14 kg for Friesian-Horro and 19.1&plusmn;0.19 kg, 42.9&plusmn;0.5and kg, 85.5&plusmn;1.5 kg for Jersey-Horro breeds, respectively. From the results suggests that birth weight of the calves doesn&rsquo;t affect the weaning weight and the subsequence growth performance. This could point to an opportunity for much improved weaning and one year weight by improving the management practices of calves. The higher growth performance observed for the crossbred calves in comparison with the purebred Horro (Bos indicus) cattle. &nbsp; Key words:&nbsp;Crosses, fixed, growth, Horro, non genetic, traits.

Genome-wide joint meta-analysis of SNP and SNP-by-smoking interaction identifies novel loci for pulmonary function.

Genome-wide association studies have identified numerous genetic loci for spirometic measures of pulmonary function, forced expiratory volume in one second (FEV1), and its ratio to forced vital capacity (FEV1/FVC). Given that cigarette smoking adversely affects pulmonary function, we conducted genome-wide joint meta-analyses (JMA) of single nucleotide polymorphism (SNP) and SNP-by-smoking (ever-smoking or pack-years) associations on FEV1 and FEV1/FVC across 19 studies (total N = 50,047). We identified three novel loci not previously associated with pulmonary function. SNPs in or near DNER (smallest P JMA = 5.00×10−11), HLA-DQB1 and HLA-DQA2 (smallest P JMA = 4.35×10−9), and KCNJ2 and SOX9 (smallest P JMA = 1.28×10−8) were associated with FEV1/FVC or FEV1 in meta-analysis models including SNP main effects, smoking main effects, and SNP-by-smoking (ever-smoking or pack-years) interaction. The HLA region has been widely implicated for autoimmune and lung phenotypes, unlike the other novel loci, which have not been widely implicated. We evaluated DNER, KCNJ2, and SOX9 and found them to be expressed in human lung tissue. DNER and SOX9 further showed evidence of differential expression in human airway epithelium in smokers compared to non-smokers. Our findings demonstrated that joint testing of SNP and SNP-by-environment interaction identified novel loci associated with complex traits that are missed when considering only the genetic main effects.

On Lung Function and Interactions Using Genome-Wide Data

On Lung Function and Interactions Using Genome-Wide Data

Gene–environment interaction in externalizing problems among adolescents: evidence from the Pelotas 1993 Birth Cohort Study

BackgroundThe study of gene–environment interactions (G × E) is one of the most promising strategies to uncover the origins of mental disorders. Replication of initial findings, however, is essential because there is a strong possibility of publication bias in the literature. In addition, there is a scarcity of research on the topic originated from low- and middle-income countries (LMIC). The aim of this study was to replicate G × E hypotheses for externalizing problems among adolescents in a middle-income country.MethodsAs part of the Pelotas 1993 Birth Cohort Study, 5,249 children were enrolled at birth and followed up to the age of 15 years, with an 85.7% retention rate. We sought an interaction between the homozygosity of the 10-repeat allele at the dopamine transporter (DAT1) gene and prenatal maternal smoking in the development of hyperactivity problems during adolescence assessed by the Strengths and Difficulties Questionnaire. We also tested for an interaction between the uVNTR polymorphism at the monoamine oxidase A (MAOA) and the experience of childhood maltreatment in the occurrence of conduct problems among adolescent boys.ResultsAlthough there was a clear association between prenatal maternal smoking and hyperactivity scores in adolescence (p < 0.001), no main genetic or interaction effects for the DAT1 gene were detected. Similarly, childhood maltreatment showed to be associated with conduct problems among boys (p < 0.001), with no observable main genetic or interaction effects for the MAOA gene.ConclusionsIn the largest mental health G × E study performed in a LMIC to date, we did not replicate previous positive findings from the literature. Despite the presence of main environmental effects, there was no evidence of effect modification by genotype status. Additional replication efforts to measure G × E are needed to better understand the origins of mental health and illness, especially in LMIC.

Logic regression analysis of association of gene polymorphisms with low HDL: Tehran Lipid and Glucose Study

IntroductionLogic regression is a generalized regression method that can recognize complex Boolean interactions of binary variables. It has been successfully applied to single-nucleotide polymorphism (SNP) data because of the importance of interactions in SNP association studies. The objective of this study is to assess the association between high-density lipoprotein (HDL) function and some related gene polymorphisms after adjusting for potential confounders using logic regression. MethodsSubjects in this cross-sectional study were randomly selected from among participants of the Tehran Lipid and Glucose Study (TLGS). A total of 436 subjects (172 men and 264 women), aged ≥20years, were selected to be included in the current study. Logic regression analysis was used in order to recognize the combination of genetic main effects and possible interactions associated with HDL-C level. Cross validation and randomization test were carried out to avoid over fitting of the models. ResultsThe cross validation test suggested three Boolean combinations with four predictors for a fully-adjusted logic model. The fully adjusted model showed that those who carry an Apo E gene E3 allele or have high TG level have an odds ratio of 2.35 (95% CI:1.3–4.25) for having low HDL compared to other subjects. In addition, subjects with high TG level have an odds ratio of 2.73 (95% CI: 1.65, 4.53) for having low HDL. ConclusionThe results showed that logic regression is a powerful method to find the interaction between high TG level and Apo E polymorphism associated with low HDL.

Abstract 05: A pilot gene-environment-wide interaction study (GEWIS) of neurocognitive impairment in childhood medulloblastoma survivors.

Abstract Introduction: Neurocognitive impairment after radiation therapy is a significant complication in cancer patients, affecting every age group; however pediatric populations are the most vulnerable. Medulloblastoma is the most common central nervous system malignancy in children, and craniospinal radiation is an essential part of standard treatment. While 5-year survival is approximately 70% due to advances in treatment, at least half of the survivors develop progressive and permanent neurocognitive impairment. To date, few risk factors for these adverse outcomes have been identified. Neurocognitive impairment is believed to be a complex process affected by genes, environmental factors, and their interactions. The genome-wide association study (GWAS) is an important method for identifying main genetic effects, that is, associations between single nucleotide polymorphisms (SNPs) and prognosis. However, scanning for main effects might miss important genetic variants among those with unique environmental exposures. Therefore, we employed a 2-step approach utilizing GWAS data for detecting loci involved in gene-environment interactions to determine predictors of neurocognitive impairment. This approach (the gene-environment-wide interaction study or GEWIS) is a powerful alternative to single-step interaction tests. Methods: Childhood medulloblastoma survivors were recruited from the long-term survivor clinics at Texas Children's Cancer Center and MD Anderson Cancer Center between 2003 and 2010. Working memory was assessed using age-appropriate intelligence scales for children, which included scores for verbal IQ (VIQ), performance IQ (PIQ), and full scale IQ (FSIQ). We defined neurocognitive impairment as a score of &amp;lt;77.5 on any of the three IQ subscales. Demographic and clinical data were abstracted from medical records. Exposure to ambient levels of benzene (a suspected environmental neurotoxicant) was estimated using the U.S. Environmental Protection Agency's Assessment System for Population Exposure Nationwide (ASPEN) model. Genotyping was conducted using the Illumina Human 1M Quad SNP Chip. Exposure and genotype data were available on 47 subjects (20 with neurocognitive impairment and 27 without neurocognitive impairment). In step 1, we examined the association between each SNP (G) and benzene exposure (E) using a Wald test based on a logistic regression model. SNPs where p &amp;lt;0.001 were then included in step 2, a case-control analysis of GxE interactions using logistic regression models with the constrained maximum-likelihood method. Analyses were conducted using the ‘CGEN’ package in R. As this was a pilot study, the false discovery rate (FDR) was controlled at 0.25. Results: Benzene exposure was positively associated with neurocognitive impairment (OR = 2.0, 95% CI: 0.39-10.16); however, the association was not statistically significant. In step 1 of the preliminary analysis, seven SNPs were associated with neurocognitive impairment (p &amp;lt;0.001). Among this set, two GxE interactions were significantly associated with neurocognitive impairment (rs1223760-benzene, p = 0.014, q = 0.085 and rs11932152-benzene, p = 0.060, q = 0.182). Interestingly, rs1223760 is an intergenic SNP between PROX1 and SMYD2, two genes suspected of influencing DNA methylation. Conclusions: To our knowledge, this is the first GEWIS of neurocognitive impairment in survivors of childhood medulloblastoma. As the genetic architecture of neurocognitive impairment is complex, it is important to explore interactions using high-dimensional data. Our results are suggestive that GxE interactions may be important in prognosis; however, the results are preliminary and must be validated in a larger and independent population. Citation Format: Philip J. Lupo, M Fatih Okcu, Renke Zhou, Lynnette L. Harris, Bartlett D. Moore, III, Michael E. Scheurer. A pilot gene-environment-wide interaction study (GEWIS) of neurocognitive impairment in childhood medulloblastoma survivors. [abstract]. In: Proceedings of the AACR Special Conference on Post-GWAS Horizons in Molecular Epidemiology: Digging Deeper into the Environment; 2012 Nov 11-14; Hollywood, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2012;21(11 Suppl):Abstract nr 05.

The gene in its natural habitat: The importance of gene–trait interactions

Despite the substantial heritability of nearly all psychological traits, it has been difficult to identify specific genetic variants that account for more than a tiny percentage of genetic variance in phenotypes. Common explanations for this "missing heritability" include massive polygenicity, rare variants, epigenetics, epistasis, and gene-environment interactions. Gene-trait (G × T) interaction is another concept useful for understanding the lack of obvious genetic main effects. Both genes and environments are distal contributors to human behavior, but the brain is the proximal driver of behavior. The effect of any single genetic variant is dependent on the configuration of the brain in which it is expressed. One method to begin studying how single genes interact with variations in the rest of the brain is to investigate G × T interactions. A psychological trait reflects a characteristic pattern of psychological function (and, therefore, of brain function), which has its origin in the cumulative effects of both the genome and the environment. A trait therefore describes variation in the broad organismic context in which any single gene operates. We describe the nature and significance of G × T interactions for understanding psychopathology and normal trait variation, which are illustrated with empirical examples.

Associations and interactions of genetic polymorphisms in innate immunity genes with early viral infections and susceptibility to asthma and asthma-related phenotypes

The innate immune system is essential for host survival because of its ability to recognize invading pathogens and mount defensive responses. We sought to identify genetic associations of innate immunity genes with atopy and asthma and interactions with early viral infections (first 12 months of life) in a high-risk birth cohort. Three Canadian family-based studies and 1 Australian population-based case-control study (n= 5565) were used to investigate associations of 321 single nucleotide polymorphisms (SNPs) in 26 innate immunity genes with atopy, asthma, atopic asthma, and airway hyperresponsiveness. Interactions between innate immunity genes and early viral exposure to 3 common viruses (parainfluenza, respiratory syncytial virus, and picornavirus) were examined in the Canadian Asthma Primary Prevention Study by using both an affected-only family-based transmission disequilibrium test and case-control methods. In a joint analysis of all 4 cohorts, IL-1 receptor 2 (IL1R2) and Toll-like receptor 1 (TLR1) SNPs were associated with atopy after correction for multiple comparisons. In addition, an NFKBIA SNP was associated with atopic asthma. Six SNPs (rs1519309 [TLR3], rs740044 [ILIR2], rs4543123 [TLR1], rs5741812 [LBP], rs917998 [IL18RAP], and rs3136641 [NFKBIB]) were significant (P< .05, confirmed with 30,000 permutations) in both the combined analysis of main genetic effects and SNP-virus interaction analyses in both case-control and family-based methods. The TLR1 variant (rs4543123) was associated with both multiple viruses (respiratory syncytial virus and parainfluenza virus) and multiple phenotypes. We have identified novel susceptibility genes for asthma and related traits and interactions between these genes and early-life viral infections.

SVM‐Based Generalized Multifactor Dimensionality Reduction Approaches for Detecting Gene‐Gene Interactions in Family Studies

Gene-gene interaction plays an important role in the etiology of complex diseases, which may exist without a genetic main effect. Most current statistical approaches, however, focus on assessing an interaction effect in the presence of the gene's main effects. It would be very helpful to develop methods that can detect not only the gene's main effects but also gene-gene interaction effects regardless of the existence of the gene's main effects while adjusting for confounding factors. In addition, when a disease variant is rare or when the sample size is quite limited, the statistical asymptotic properties are not applicable; therefore, approaches based on a reasonable and applicable computational framework would be practical and frequently applied. In this study, we have developed an extended support vector machine (SVM) method and an SVM-based pedigree-based generalized multifactor dimensionality reduction (PGMDR) method to study interactions in the presence or absence of main effects of genes with an adjustment for covariates using limited samples of families. A new test statistic is proposed for classifying the affected and the unaffected in the SVM-based PGMDR approach to improve performance in detecting gene-gene interactions. Simulation studies under various scenarios have been performed to compare the performances of the proposed and the original methods. The proposed and original approaches have been applied to a real data example for illustration and comparison. Both the simulation and real data studies show that the proposed SVM and SVM-based PGMDR methods have great prediction accuracies, consistencies, and power in detecting gene-gene interactions.